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Label docstrings with versionadded / versionchanged

See original GitHub issue

We should be using Sphinx’s versionadded and versionchanged directives to indicate when classes, functions, methods and parameters are added or modified in their semantics. We are not very good at ensuring this labelling is done.

Towards the upcoming release of v0.22, it would be useful if some contributors (or sprinters) scoured the change log and identified whether any added/changed parameters/classes needed a versionadded or versionchanged label, and to produce pull requests adding them when otherwise omitted.

I also note past omissions, for example KBinsDiscretizer and IterativeImputer do not mention their versionadded, while ColumnTransformer does. Recording for each estimator when it was first released would be helpful for users when looking at the documentation.

Suggested procedure

Check the changelog in doc/whats_new/ one what’s a new file at a time. **Make a single PR for an estimator and a specific version. Do not include multiple estimators and multiple versions at once (it makes it difficult to review).

Focus on Public API: if a method has been made private, check the tickbox and move to another.

.. versionadded:: 0.xx

should be included only for new estimators/parameters

.. versionchanged:: 0.xx
   `param_xxx` change from 'xx' to 'yy'

should be included when the default value of a parameter change.

Classes lacking label

The list may be incomplete.

ARDRegression PRs #15482 #15973 #16015
AdaBoostClassifier
AdaBoostRegressor
AdamOptimizer
AdditiveChi2Sampler
AffinityPropagation
AgglomerationTransform
BaggingRegressor
BaseBagging
BaseCrossValidator
BaseDecisionTree
BaseDiscreteNB
BaseEnsemble
BaseForest
BaseGradientBoosting
BaseHistGradientBoosting
BaseLabelPropagation
BaseLibSVM
BaseLoss
BaseMixture
BaseNB
BaseRandomProjection
BaseSGD
BaseSVC
BaseSearchCV
BaseShuffleSplit
BaseSpectral
BaseWeightBoosting
BernoulliNB
BernoulliRBM
Binarizer
BinaryCrossEntropy
BinomialDeviance
BinomialDeviance
Birch
Bunch
CCA
CalibratedClassifierCV
CategoricalCrossEntropy
CategoricalNB
ChangedBehaviorWarning
CheckingClassifier
ClassificationLossFunction
ClassifierChain
ComplementNB
ConvergenceWarning
CountVectorizer
DataConversionWarning
DataDimensionalityWarning
DictVectorizer
DummyRegressor
ElasticNet
ElasticNetCV
EllipticEnvelope
EmpiricalCovariance
ExponentialLoss
ExponentialLoss
FactorAnalysis
FastICA
FeatureHasher
FeatureUnion
FitFailedWarning
ForestClassifier
ForestRegressor
GaussianNB
GaussianRandomProjection
GenericUnivariateSelect
GraphicalLasso
GraphicalLassoCV
GridSearchCV
GroupKFold
HistGradientBoostingClassifier
HistGradientBoostingRegressor
HuberLossFunction
IncrementalPCA
IsotonicRegression
IterativeImputer Open PR
KBinsDiscretizer
KFold
KMeans
KNeighborsClassifier
KNeighborsRegressor
KernelCenterer
KernelDensity
KernelRidge
KeyValTuple
KeyValTupleParam
LabelBinarizer
LabelEncoder
LabelPropagation
LabelSpreading
Lars
LarsCV
Lasso
LassoCV
LassoLars
LassoLarsCV
LassoLarsIC
LeastAbsoluteDeviation
LeastAbsoluteError
LeastSquares
LeastSquaresError
LeaveOneGroupOut
LeaveOneOut
LeavePGroupsOut
LeavePOut
LedoitWolf
LinearClassifierMixin
LinearModel
LinearModelCV
LinearRegression
LinearSVC
LinearSVR
LocalOutlierFactor
LocallyLinearEmbedding
LossFunction
MDS
MinCovDet
MiniBatchKMeans
MissingIndicator Open PR
Module_six_moves_urllib
Module_six_moves_urllib_error
Module_six_moves_urllib_parse
Module_six_moves_urllib_request
Module_six_moves_urllib_response
Module_six_moves_urllib_robotparser
MultiLabelBinarizer
MultiOutputClassifier
MultiOutputRegressor
MultiTaskElasticNet
MultiTaskElasticNetCV
MultiTaskLasso
MultiTaskLassoCV
MultinomialDeviance
MultinomialNB
NearestCentroid
NearestNeighbors
NeighborhoodComponentsAnalysis
NeighborsBase
NoSampleWeightWrapper
NonBLASDotWarning
Normalizer
NotFittedError
NuSVR
Nystroem
OAS
OPTICS
OneClassSVM
OneHotEncoder
OneVsOneClassifier
OneVsRestClassifier
OrdinalEncoder
OrthogonalMatchingPursuit
OrthogonalMatchingPursuitCV
OutputCodeClassifier
PLSCanonical
PLSRegression
PLSSVD
PatchExtractor
Pipeline
PowerTransformer
PredefinedSplit
QuantileLossFunction
QuantileTransformer
RBFSampler
RFE
RFECV
RadiusNeighborsClassifier
RadiusNeighborsRegressor
RandomizedSearchCV
RegressionLossFunction
RegressorChain
RepeatedKFold
RepeatedStratifiedKFold
RidgeCV
RidgeClassifierCV
SGDOptimizer
SVR
ScaledLogOddsEstimator
SelectFdr
SelectFpr
SelectFwe
SelectKBest
SelectPercentile
SelectorMixin
ShrunkCovariance
ShuffleSplit
SkewedChi2Sampler
SkipTestWarning
SimpleImputer Open PR
SparseCodingMixin
SparseRandomProjection
SpectralBiclustering
SpectralClustering
SpectralCoclustering
SpectralEmbedding
StratifiedKFold
StratifiedShuffleSplit
TfidfTransformer PRs #15482 #15973 #16015
TfidfVectorizer PRs #15482 #15973 #16015
TheilSenRegressor
TimeSeriesSplit
TransformedTargetRegressor
TruncatedSVD
UndefinedMetricWarning
VarianceThreshold

Issue Analytics

State:
Created 4 years ago
Comments:38 (24 by maintainers)

Top GitHub Comments

1reaction

adrinjalalicommented, Apr 8, 2020

I think we’ve covered most of the relevant ones. I personally wouldn’t worry about anything older than 0.18

1reaction

thomasjpfancommented, Nov 2, 2019

Do we need a policy regarding really old versions? I do not know if versionadded: 0.5 is helpful.

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